Meta-QTLs and candidate genes for kernel protein content in maize

Ke Li, Zilong Zhao, Wei Wang, Zhuoyang Li, Fei Yi, Jinsheng Lai, Qiujie Liu, Hainan Zhao & Jian Chen

Theoretical and Applied genetics; March 6 2026; vol. 139; article 90

Maize is a major feed and food crop, yet its kernel protein content remains insufficient to meet nutritional needs. Enhancing protein content is critical for improving feed efficiency and supporting plant-based diets. Although numerous QTLs associated with protein content have been identified, their typically broad confidence intervals and variability across studies make it difficult to pinpoint reliable candidate genes. Therefore, we performed a comprehensive meta-QTL (MQTL) analysis by integrating data from 25 QTL studies, encompassing 258 initial QTLs related to maize kernel protein content. A high-density consensus genetic map was constructed by merging 23 genetic maps and incorporating 19,836 markers across IBM2_2008_Neighbors reference genetic map. A total of 67 MQTLs were identified, each integrating an average of 5 initial QTLs, with their confidence intervals reduced about 2.51-fold on average. Notably, 18 MQTLs had physical intervals shorter than 1 Mb, indicating high mapping precision. To validate the MQTLs, genome-wide association studies (GWAS) on maize protein content were integrated, and 15 MQTLs were colocated with GWAS signals. Further, based on expression patterns and homology analysis across cereal crops, 54 core candidate genes were identified, including Aaap54, Tar3 and EREB133, which their homolog in rice or soybean has been demonstrated to play an important role in regulating protein accumulation. These findings demonstrate the utility of meta-QTL analysis in refining complex trait architecture and provide a valuable foundation for functional genomics studies and the genetic improvement of maize kernel protein content.

See https://link.springer.com/article/10.1007/s00122-026-05199-5